How Learning Affects Firm s Export Entry Decisions. (Preliminary

Transcription

1 How Learning Affects Firm s Export Entry Decisions. (Preliminary Version) Beverly Mendoza * August 10, 2018 Exporters face uncertainty upon entering a new market, and how a firm resolves that uncertainty dictates the timing of its entry into a new market. Through the construction of the model, this paper examines how the firms export entry decisions are affected by the two channels of learning learning from their own experience and from other firms, and it finds that (i) firms are quicker at resolving uncertainty when its previously entered markets are highly correlated with its potential market; and (ii) observing more firms entering the market provides a more accurate signal of the market demand. Using data from Chilean exports that spans from 2003 to 2016, the paper discovers that firms with larger networks (more tested destinations) enter the new market sooner while observing more firms further delay the firm s entry. Keywords: Firm Heterogeneity, Knowledge Spillover, Uncertainty, Learning to Export, Exporting Delays, JEL classifcation: F12; F14 *Indiana University, Wylie Hall Rm 204, 100 S. Woodlawn, Bloomington, IN ; bemendoz@indiana.edu

2 1 INTRODUCTION Exporters face uncertainty upon entering a new foreign market. This uncertainty causes these firms to experience some delay in their entry. Current trade models predict that once the uncertainty is resolved, the most productive firms enter the market; however, there has been little empirical evidence supporting this prediction. Empirical research has shown that firms do not export simultaneously, which papers have attributed to search costs, trade costs, barriers to entry, and learning. This paper examines how the firms export entry decisions are affected by the two channels of learning learning from own experience and from other firms. According Melitz (2003), the most productive firm enters the market followed by the next productive firm, however this prediction has little empirical support 1. Firms usually cannot observe the productivity of other firms, however they are able to observe the export revenue of these firms and possibly estimate an average level of productivity. Since firms are able to easily observe transactions data from other exporters due to the accessibility of this information, then firms would be able to learn from these other firms servicing a particular market. More specifically, a firm who is interested in entering a new market could infer its demand from the exporters who are already servicing that market. However, firms not only learn from other firms but from their own experience as well. Nguyen (2012) examines the reasons behind export entry delay to foreign markets, while Albornoz et al. (2012) studies the value of testing out the cheaper market first before exporting simultaneously. In this line of research, the firms are able to infer the demand for their product from their own experience in already tested markets. Since they know their performance in other markets, they are equipped with an idea of how their product will fare in a new market. In this paper, I reconcile the two channels of learning learning from other firms and learning from own experience. The model presented in this paper is able to show how a firm s export decision is dependent on learning from other firms and its own experience. To my knowledge, this is the first paper that studies the two channels of learning interdependently. With technology and digitation of records, acquiring information on the exports of other firms is more accessible. Although information on other firms is readily acquired by firms, exporters still value their own experience in other markets. Examining these two channels together provides insight on how much a firm should trust its own experience compared to other firms experience. Previous research have explored the possible reasons for the delay in entering a new market. Some papers have focused on experimentation and learning from the firm s own experience as the channel that causes entry delay 2, while others look into learning from other firms experience 3. In this paper, I 1 See Nguyen (2012), Segura-Cayuela and Vilarrubia (2008). 2 See Albornoz et al. (2012), Nguyen (2012) 3 See Fernandes and Tang (2014), Segura-Cayuela and Vilarrubia (2008) 1

3 reconcile these two channels of learning by analyzing the entry behavior of firms. 2 THEORETICAL MODEL The model for this paper is an extension of the model presented in Fernandes and Tang (2014). In their paper, Fernandes and Tang (2014) assumes that the firm-market-specific uncertainty is only resolved during the firm s first year of exporting, and the potential entrants cannot infer this variable from anywhere else. This paper augments their model to sequential exporting where it also considers the potential entrant s previous experience in other markets. Most of the exporters that are observed in the literature are seasoned exporters (exporters who cater to multiple destinations), thus there is a great expansion in research where trade economists study the exporters expansion to other markets based on their cumulative experience. This paper pushes this area of research into marrying the two channels of learning learning from neighbors and the firm s own experience. To my knowledge, this is the first study that examines the relationship between these two learning channels. 2.1 MODEL SET-UP Consider a Melitz-type framework where the market is monopolistically competitive with CES preferences. There are N potential entrants with heterogenous firm productivity, φ > 0. Since this paper mainly focuses on the firm s entry decisions, the model is generally highlights two periods: learning and decision periods. The firm gathers information during the first period and acts upon it during the next time period. Since this paper is generally interested in studying firm decisions, consider a firm i with productivity φ who is wanting to serve market m. Its profit function is expressed as π(d im φ) = D im φ σ 1, (2.1) where σ is the elasticity of substitution. The firm faces a demand that is expressed as ln(d im ) = κ + d m + z im (2.2) where κ is a constant demand shifter, d m is the market-specific attribute, and z im is the firm-market specific variable. This demand function shows that the firm is faced with two channels of uncertainty when deciding on its entry to a new market m market uncertainty (d m ) and firm-market uncertainty (z im ). Market uncertainty, d m is common across firms, which implies that this can be inferred from the neighbors experience in market m. The firm-market specific uncertainty, z im, is distinct to the firm, meaning that the firm cannot draw inference from its neighbors experience. One can think of this type 2

4 of uncertainty as the firm s product appeal to the market. In other words, z im captures the appeal of the firm s product in the market. Even though there are multiple firms servicing market m, firm i s product could be received differently from its neighbors. Since this firm-market uncertainty is specific to the firm, it cannot be inferred from its neighbors, but it can be inferred from the firm s own experience from previously entered markets. Knowing its product appeal in other markets provides the firm information on how receptive consumers are of its product. For example, if firm i has exported to Canada and it finds that its product appeal was high, then it will also expect to have a high product appeal in the US. Since countries are better connected now through the rise of globalization, it is reasonable to assume that markets are correlated. In other words, a good performance in market m implies that the firm is likely to have a good performance in market j, given that these to markets are positively correlated 4. Furthermore, the model assumes that the demand, ln(d im ), is time invariant, meaning that the demand does not change over time in the absence of random shocks. Once a firm learns the demand that it is faced with, it adjusts its export decisions accordingly. However, in order to avoid complicating the model with strategic interactions, the model does not track how the exporter adjusts its shipments once it learns the market. Instead, the model mainly abstracts from the concept of strategic interactions 5. Additionally, the model assumes that the productivity, φ, and firm-market variable, z im, are independently distributed. This assumption implies that the products made by highly productive firms are necessarily received well in the foreign market. In other words, there is a possibility that the product appeal of a productive firm could be low. Given this level of uncertainty, it is reasonable to assume that productivity and product appeal are independent between each other. The prior beliefs of firm i are as follows d m N ( d m, v dm ) z im N ( z im, v zm ) (2.3) Without any given information, the (unconditional) expected profit of firm i can be further expressed as π(d im φ) = D im φ σ 1 = ξφ σ 1 exp ( dm + z im + v m 2 ), (2.4) where ξ = exp(κ) and v m = v dm + v zm. From (2.4), it can be observed that the (unconditional) expected profit of firm i is dependent on both the expectations on market demand and product appeal. Also, (2.4) shows that the firm has a higher expected profit with larger variance since it encourages experimentation due to higher upside in sales. 4 see Albornoz et al. (2012), Nguyen (2012) 5 Further studies regarding the strategic interactions of exporters is yet to be explored based on my knowledge, thus far. 3

5 Moreover, each firm incurs a sunk cost, Km e, upon entry to market m. This sunk cost provides the ex-ante zero profit condition along with the expected profit, (2.4). The ex-ante zero profit condition implies that firm i will only enter when its expected profit is at least as much as its sunk cost. In other words, E [ π(d im φ ] = Km e ( ξφ σ 1 exp dm + z im + v ) m = Km e 2 (2.5) Rearranging (2.5) derives the productivity threshold = φ = ( K m ξ ( dm + z im + v m 2 ) ) 1 σ 1 (2.6) This productivity threshold shows the minimum level of productivity required for a firm to enter the market. From 2.6, it can be observed that this threshold decreases when there is a higher market-specific demand (d m ); the result is the same when there is the product appeal is high. Although this observation is rather intuitive, it is important to note that a higher product appeal lowers the threshold only for a particular firm, which can explain why there is an entry delay into exporting in a new market. In Fernandes and Tang (2014), their model predicts that all potential entrants will enter the foreign market once there is a positive shock into d m, however, this model further extends this into a firm-specific result. In particular, this model shows that entry into a foreign market is firm-specific when it accounts for the firm s own experience into its entry decision. 2.2 LEARNING FROM OTHER FIRMS Suppose firm i is deciding to enter market m and it observes n m,t 1 firms who have entered the market prior to period t. The firm also observes the average export revenue, R m,t 1, and it knows the time varying conditional mean of the other firms productivity ˆφ t 1 = E [ φ φ σ 1 > φ t 1 ] (2.7) Lastly, the model assumes that the firms have limited memory; In particular, firms cannot recall φ t k for all k > 1. Based on the knowledge of its own productivity (φ), number of other firms (n m,t 1 ), and their average revenue ( R m,t 1 ), firm i infers the level of demand in market m d lear n m,t 1 = ( R m,t 1 /n m,t 1 ) ˆφ σ 1 t 1 (2.8) 4

6 One can think of 2.8 as firm i learning about the average demand of market m which is inferred from the already existing firms in that market. Following DeGroot (2005), the posterior belief regarding market demand, d m, is normally distributed with mean ( d post n m,t 1, mt dm,t 1 lear n ) [ = E d mt n m,t 1, dm,t 1 lear n ] = δ t d lear n m,t 1 + (1 δ t ) d m (2.9) where δ t acts as the weight on learning from other firms. The weight δ can be derives as ( ) n m,t 1 v dm δ t nm,t 1, v dm, v zm = v zm + n mt v dm ( ) 1 v 1 (2.10) zm = 1 + n m,t 1 v dm Provided with information from other firms, the conditional variance of d m t is ( ) v zm v dm v mt nm,t 1, v dm, v zm = v zm + n m,t 1 v dm ( 1 = + n ) 1 (2.11) m,t 1 v dm v zm 2.3 LEARNING FROM OWN EXPERIENCE Suppose that firm i has entered J markets where J [0, M] prior to period t. Based on knowing its productivity, φ, product appeal in J markets, z J i = (z i 1,..., z i J ), and the number of tested markets, J, the firms infers its product appeal for market m which provides the posterior mean ( )( ) z post im = j J z i j Jρ J Jρ + (1 ρ) (2.12) where ρ is the correlation among markets. For simplicity, this model assumes that all markets are correlated equally for a specific firm, however, this correlation might be differ for each firm. The reason why the correlation is firm-specific is because of the firms sell different products and serve different destinations from each other. For example, a firm who has been exporting to latin american countries is deciding to ship to China; since the reception of the product be completely different in China, the correlation would then be different. Conversely, if the firm is now deciding to export to another latin american country (instead of China), then the correlation coefficient would generally be higher. If firm i has never exported to any other destination, i.e., J = 0, then E(z im ) = 0 with var (z im ) = v zm. On the other hand, if the firm has exported to all M countries, then E(z im ) = z im. However, if the firm 5

7 has exported to J markets such that J (0, M), then 6 ( )( ) j J z i j Jρ E(z im J) = with, (2.13) J Jρ + (1 ρ) Jρ var (z im J) = v zm (1 2 ) (2.14) Jρ + (1 ρ) 2.4 COMPARATIVE STATICS var (z im J) J = v zm(ρ 1)ρ 2 ((J 1)ρ + 1) 2 0 (2.15) Since the correlation ρ [0,1], then (2.15) 0; this suggests that an increase in the number of markets entered decreases the variance. In particular, a firm is quicker to resolve its product appeal uncertainty in entering market m when it has entered many other destinations prior to period t. var (z im J) ρ = v zm Jρ((J 1)ρ + 2) ((J 1)ρ + 1) 2 0 (2.16) 2.16 reveals that an increase in the correlation coefficient, ρ, decreases the variance, meaning that the firm is able to resolve its uncertainty quicker when market m is more correlated to its other previous destinations. 3 DATA This paper mainly examines products that have been pioneered from Chile during A pioneered product is defined as a product that has been exported from Chile for the first time; more specifically, these products have never been exported before. 7 Focusing on pioneered products provides a more accurate identification strategy for this study because it is able to pinpoint entry into a new market. Since pioneered products are those that have never been exported before, this study can accurately observe firm entry decisions into a new market. The data used in this paper is primarily composed of two main datasets the Comtrade Chilean dataset and the Chilean customs dataset. The combination of these two datasets provides detail on accurately identifying a pioneered product, which is important because the Comtrade dataset can track the pioneered products in the customs dataset. Focusing solely on the customs dataset will bias the results since this dataset only spans from , which means that it is un- 6 Following... 7 This study focuses on products at the HS6 level. 6

8 known whether a product has been exported prior to In order to alleviate this concern, I track the products present in the customs dataset with the Comtrade dataset which starts from Utilizing the Comtrade dataset is crucial in identifying the pioneered products present in the customs dataset. In order to identify the two channels of learning, the dataset has been further restricted to only examine pioneered products with firms following the pioneers to the same destination market. In the following section, I include the products with no followers. 3.1 DATA SUMMARY - PRODUCTS WITH FOLLOWERS ONLY This section focuses on the pioneered products with firms that entered after the product was first exported. In this section, it is ideal to focus on these follower firms since they are able to showcase their decisions upon entering a new market. Variable Total Number of Observations 1,705 Time Period Number of Firms 198 Number of Followers 116 Number of New Market Destinations 20 Number of Pioneered Products 70 Table 3.1: Data Summary Mean Median 25p 75p Min Max Followers per Variety Destinations upon Entry per Firm Table 3.2: Data Distribution 3.2 EMPIRICAL TESTS Entry Delay (in months) j mt = α + β(destinations, Other Firms) t 1 + Y ear F E + Industr yf E + µ j mt (3.1) 3.1 shows how destinations and firms affect the firm s entry decision with product j to market m at time t. In this equation, Destinations refer to the number of destinations the firm has exported to since The variable Other Firms refer to the number of firms who have exported product j to market m prior to time t. The results of implementing equation (3.1) is shown in Table 3.3. From this simple regression, it is observed that firms with a larger network more previously tested destinations enter the market sooner, while observing other firms creates additional entry delay. Firms who observe other exporters 7

9 prior to entering experience a greater entry delay since the information received from the other firms becomes more accurate as the number of observed entering firms increases. Independent Variable: Entry Delay (in months) Destinations ** *** (0.146) (0.196) Other Firms 2.732*** 2.569*** (0.110) (0.096) Number of Observations 1,705 1,376 Year FE Y Y Industry FE Y Y Followers Only N Y Table 3.3: Panel Regressions on Firm Entry Delay. (Standard errors are in parentheses. ***p<0.001, **p<0.01, *p<0.05, p<0.1) 3.3 DATA SUMMARY - INCLUDING PRODUCTS WITH NO FOLLOWERS Variable Total Number of Observations 2,357 Time Period Number of Firms 341 Number of Followers 116 Number of New Market Destinations 45 Table 3.4: Data Summary Mean Median 25p 75p Min Max Firms per Variety Followers per Variety Destinations upon Entry per Firm Table 3.5: Data Distribution 8

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